0.背景知识一点点
oncoPredict是根据基因表达量来预测药物敏感性的R包。也就是说它可以根据你的样本基因表达量来告诉你每个药物的IC50值，这个值越低就说明药物越管用。
提到药物预测，还有一个pRRophetic包，建议不用看了，因为oncoPredict是它的plus版本。
还有一个cellMiner网站，之前写过，可以翻翻看。
1.载入数据
代码参考自：https://mp.weixin.qq.com/s/QRaTd-fIsqq6sPsLmOPvIw，一些背景知识也可以补充下.
在Training Data文件夹下存放着R包作者准备好的数据，用作药物预测的训练集。下载自：https://osf.io/c6tfx/
rm(list = ls())
library(oncoPredict)
library(data.table)
library(gtools)
library(reshape2)
library(ggpubr)
dir='./DataFiles/DataFiles/Training Data/'
dir(dir)
## [1] "CTRP2_Expr (RPKM, not log transformed).rds"
## [2] "CTRP2_Expr (TPM, not log transformed).rds"
## [3] "CTRP2_Res.rds"
## [4] "GDSC1_Expr (RMA Normalized and Log Transformed).rds"
## [5] "GDSC1_Res.rds"
## [6] "GDSC2_Expr (RMA Normalized and Log Transformed).rds"
## [7] "GDSC2_Res.rds"
可以看到其中包括了Cancer Therapeutics Response Portal (CTRP)和Genomics of Drug Sensitivity in Cancer (GDSC)，我们直接用v2
两个数据库的数据，都是提供了基因表达矩阵和药物IC50表格。
exp = readRDS(file=file.path(dir,'GDSC2_Expr (RMA Normalized and Log Transformed).rds'))
exp[1:4,1:4]
## COSMIC_906826 COSMIC_687983 COSMIC_910927 COSMIC_1240138
## TSPAN6 7.632023 7.548671 8.712338 7.797142
## TNMD 2.964585 2.777716 2.643508 2.817923
## DPM1 10.379553 11.807341 9.880733 9.883471
## SCYL3 3.614794 4.066887 3.956230 4.063701
dim(exp)
## [1] 17419 805
drug = readRDS(file = file.path(dir,"GDSC2_Res.rds"))
drug <- exp(drug) #下载到的数据是被log转换过的，用这句代码逆转回去
drug[1:4,1:4]
## Camptothecin_1003 Vinblastine_1004 Cisplatin_1005
## COSMIC_906826 0.3158373 0.208843106 1116.05899
## COSMIC_687983 0.2827342 0.013664227 26.75839
## COSMIC_910927 0.0295671 0.006684071 12.09379
## COSMIC_1240138 7.2165789 NA NA
## Cytarabine_1006
## COSMIC_906826 18.5038719
## COSMIC_687983 16.2943594
## COSMIC_910927 0.3387418
## COSMIC_1240138 NA
dim(drug)
## [1] 805 198
identical(rownames(drug),colnames(exp))
## [1] TRUE
drug是药物IC50值，exp是对应细胞系基因的表达矩阵。可以看到二者的样本名称是对应的。
2.操练一下
搞一个示例数据，从矩阵里面直接随机取了4个样本。
test<- exp[,sample(1:ncol(exp),4)]
test[1:4,1:4]
## COSMIC_1290797 COSMIC_906830 COSMIC_907314 COSMIC_907068
## TSPAN6 8.196623 5.542645 6.960978 6.896404
## TNMD 2.692706 2.736643 3.038283 2.774103
## DPM1 10.829487 9.890112 9.912911 10.757162
## SCYL3 3.840380 3.346422 3.845654 4.490674
colnames(test)=paste0('test',colnames(test))
dim(test)
## [1] 17419 4
运行时间很长，所以if(F)注释掉。
if(F){
calcPhenotype(trainingExprData = exp,
trainingPtype = drug,
testExprData = test,
batchCorrect = 'eb', # "eb" for array,standardize for rnaseq
powerTransformPhenotype = TRUE,
removeLowVaryingGenes = 0.2,
minNumSamples = 10,
printOutput = TRUE,
removeLowVaringGenesFrom = 'rawData' )
}
R包Vignette里关于batchCorrect参数的说明
batchCorrect options: “eb” for ComBat, “qn” for quantiles normalization, “standardize”, or “none”
“eb” is good to use when you use microarray training data to build models on microarray testing data.
“standardize is good to use when you use microarray training data to build models on RNA-seq testing data (this is what Paul used in the 2017 IDWAS paper that used GDSC microarray to impute in TCGA RNA-Seq data, see methods section of that paper for rationale)
R包Vignette里关于removeLowVaringGenesFrom参数的说明
Determine method to remove low varying genes. #Options are ‘homogenizeData’ and ‘rawData’ #homogenizeData is likely better if there is ComBat batch correction, raw data was used in the 2017 IDWAS paper that used GDSC microarray to impute in TCGA RNA-Seq data.
也就是说，芯片数据就用上面代码里的参数，转录组数据的话，就将batchCorrect改为standardize
removeLowVaringGenesFrom,作者说的也模糊啊。随便吧。
3.看看结果
这是运行之后的结果，被存在固定文件夹calcPhenotype_Output下。文件名也是固定的DrugPredictions.csv。因此一个工作目录只能计算一个数据，你可别混着用哦。
library(data.table)
testPtype <- read.csv('./calcPhenotype_Output/DrugPredictions.csv', row.names = 1,check.names = F)
testPtype[1:4, 1:4]
## Camptothecin_1003 Vinblastine_1004 Cisplatin_1005
## testCOSMIC_688011 0.10760213 0.11167741 38.54915
## testCOSMIC_687586 0.07044805 0.01858011 20.74525
## testCOSMIC_1290795 0.10672687 0.02699725 43.80543
## testCOSMIC_909709 0.14925178 0.03303756 36.73508
## Cytarabine_1006
## testCOSMIC_688011 8.099842
## testCOSMIC_687586 4.612872
## testCOSMIC_1290795 13.370822
## testCOSMIC_909709 10.393066
dim(testPtype)
## [1] 4 198
identical(colnames(testPtype),colnames(drug))
## [1] TRUE
198种药物IC50的预测结果就在这个表格里啦。
可以画个图比较一下预测结果与真实数据，可以肉眼计算相关性系数基本是1，也就知道了计算的结果确实是IC50值，而且计算的还挺准。（当然准啦，因为数据是从矩阵里面截取的）
library(stringr)
p = str_remove(rownames(testPtype),"test")
a = t(rbind(drug[p,],testPtype))
a = a[,c(1,5,2,6,3,7,4,8)]
par(mfrow = c(2,2))
plot(a[,1],a[,2])
plot(a[,3],a[,4])
plot(a[,5],a[,6])
plot(a[,7],a[,8])
